Reverso

bontojr/Reverso

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Author	SHA1	Message	Date
Junior B.	75f5383961	Docking baseline: toolchain solved, raw affinity is size-biased §12.3-12.4 first binding result on ARM Mac. - Toolchain SOLVED: AutoDock Vina 1.2.5 mac binary (Rosetta) + open-babel (brew). No conda, no MLX. dock_positive_controls.py runs end-to-end. - Cross-dock known binders + negatives into Hb (5E83) and PKR (8XFD), box centered on co-crystal ligands (5L7=voxelotor, WV2=mitapivat). Finding: raw Vina affinity ranks almost perfectly by MOLECULAR SIZE (mitapivat > voxelotor > decitabine/caffeine > hydroxyurea) in both pockets — mitapivat wins even on hemoglobin it doesn't target. Raw score can't distinguish target-specific binding: the docking analog of the connectivity specificity problem. Next: redocking-RMSD validation + ligand-efficiency normalization. Note: machine is 24GB (not 96GB per PLAN §2), capping local AF3-class inference. tools/ gitignored (vina binary). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-24 00:03:00 +02:00
Junior B.	b731478f5d	Scaffold Reverso MVP pipeline structure Set up the project skeleton per PLAN.md §4: - src/ package: identifiers, disease, drugs, scoring, provenance with pydantic schemas and confidence-tier logic (working); data-pull/compute functions stubbed per their build week - 5 starter notebooks (01-05) with PLAN-referenced steps - tests/test_scoring.py: tier-assignment tests pass; scoring reference test xfail until Week 3 - docs/: recovery_test_report, data_sources, known_limitations skeletons - pyproject.toml (requires-python >=3.11,<3.14), .gitignore, README - data/ tree preserved via .gitkeep; raw/processed/results gitignored Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-23 20:20:09 +02:00

Author

SHA1

Message

Date

Junior B.

75f5383961

Docking baseline: toolchain solved, raw affinity is size-biased

§12.3-12.4 first binding result on ARM Mac.
- Toolchain SOLVED: AutoDock Vina 1.2.5 mac binary (Rosetta) + open-babel
  (brew). No conda, no MLX. dock_positive_controls.py runs end-to-end.
- Cross-dock known binders + negatives into Hb (5E83) and PKR (8XFD),
  box centered on co-crystal ligands (5L7=voxelotor, WV2=mitapivat).

Finding: raw Vina affinity ranks almost perfectly by MOLECULAR SIZE
(mitapivat > voxelotor > decitabine/caffeine > hydroxyurea) in both
pockets — mitapivat wins even on hemoglobin it doesn't target. Raw score
can't distinguish target-specific binding: the docking analog of the
connectivity specificity problem. Next: redocking-RMSD validation +
ligand-efficiency normalization.

Note: machine is 24GB (not 96GB per PLAN §2), capping local AF3-class
inference. tools/ gitignored (vina binary).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

2026-06-24 00:03:00 +02:00

Junior B.

b731478f5d

Scaffold Reverso MVP pipeline structure

Set up the project skeleton per PLAN.md §4:
- src/ package: identifiers, disease, drugs, scoring, provenance
  with pydantic schemas and confidence-tier logic (working);
  data-pull/compute functions stubbed per their build week
- 5 starter notebooks (01-05) with PLAN-referenced steps
- tests/test_scoring.py: tier-assignment tests pass; scoring
  reference test xfail until Week 3
- docs/: recovery_test_report, data_sources, known_limitations skeletons
- pyproject.toml (requires-python >=3.11,<3.14), .gitignore, README
- data/ tree preserved via .gitkeep; raw/processed/results gitignored

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

2026-06-23 20:20:09 +02:00

2 Commits